Enhancing Cybersecurity in Smart Building Sensor Networks through AI-Driven Intrusion Detection

Enhancing Cybersecurity in Smart Building Sensor Networks through AI-Driven Intrusion Detection | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Enhancing Cybersecurity in Smart Building Sensor Networks through AI-Driven Intrusion Detection Muhammad Abdullah Nadeem This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7635973/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The Internet of Things (IoT) is increasingly integrated into smart buildings to enhance automation, efficiency, and occupant experience. However, these distributed sensor networks introduce significant cybersecurity risks. This paper presents an Artificial Intelligence–based Intrusion Detection System (AI-IDS) that leverages supervised machine learning algorithms, including Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), and Decision Tree (DT). The system is trained on two benchmark datasets, CICIDS2017 and IoT-23, with preprocessing techniques such as Synthetic Minority Oversampling Technique (SMOTE), Principal Component Analysis (PCA), and Recursive Feature Elimination (RFE) applied to improve performance. Experimental results demonstrate that the SVM model achieved the highest detection accuracy (99.02%) and offered the best balance between accuracy and training time. These findings indicate that the proposed AI-IDS can provide efficient, real-time security for smart building environments, enhancing resilience against evolving cyber threats. AI-IDS Intrusion Detection IoT Security Machine Learning Smart Buildings Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7635973","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":516280284,"identity":"5e771221-a495-4d44-86a8-6a74bccc9b08","order_by":0,"name":"Muhammad Abdullah Nadeem","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0007-0394-0101","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"Abdullah","lastName":"Nadeem","suffix":""}],"badges":[],"createdAt":"2025-09-17 05:48:00","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7635973/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7635973/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91588853,"identity":"52e5905d-8fcc-4181-a100-05f46f86f8f2","added_by":"auto","created_at":"2025-09-18 06:19:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":459905,"visible":true,"origin":"","legend":"","description":"","filename":"smartbuildingcybersecurityaiids.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7635973/v1_covered_a64b06bb-e20c-4348-a755-3fe7e26af465.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eEnhancing Cybersecurity in Smart Building Sensor Networks through AI-Driven Intrusion Detection\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"AI-IDS, Intrusion Detection, IoT Security, Machine Learning, Smart Buildings","lastPublishedDoi":"10.21203/rs.3.rs-7635973/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7635973/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Internet of Things (IoT) is increasingly integrated into smart buildings to enhance automation, efficiency, and occupant experience. 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